[4F1-GS-3] Knowledge utilization and sharing:

[4F1-GS-3-04] Investigating Knowledge Graph Completion Techniques Using Masked Language Modeling

〇Ryusei Horimoto¹, Makoto Okada², Naoki Mori² (1. Osaka Prefecture University, 2. Osaka Metropolitan University)

Keywords:AI, Knowledge Graph, BERT, Masked Language Modeling, Deep Learning

In recent years, with the rapid development of artificial intelligence technology, Knowledge Graph, which systematically connects various kinds of human knowledge and expresses their relationships in a graph structure, has attracted much attention and is used as a fundamental technology for artificial intelligence in various fields. In this context, there is a need for an automatic complementation method of Knowledge Graphs to meet the demand for adding new knowledge to existing Knowledge Graphs. The problem with conventional Knowledge Graph completion methods such as TransE and ComplEx is that they focus on knowledge relationships and do not effectively capture the semantic information of the knowledge itself. In this study, we proposed an automatic Knowledge Graph completion method using Masked Language Modeling by BERT, which is a deep language model, to effectively capture the semantic information of knowledge itself, and verified its effectiveness through evaluation experiments.